Automatic detection and cleansing of erroneous concepts in an aggregated knowledge base

ABSTRACT

A mechanism is provided for automatically detecting and cleansing erroneous concepts in an aggregated knowledge base. A graph data structure representing the concept present in a portion of the natural language content is generated. The graph data structure is analyzed to determine whether or not the graph data structure comprises one or more concept conflicts in association with a set of nodes in the graph data structure, the one or more concept conflicts are associated with the set of nodes if two or more nodes represent separate and distinct concepts. Responsive to determining that there are one or more concept conflicts due to there being two or more nodes representing separate and distinct concepts, the two or more nodes are split into separate distinct concepts within the knowledge base.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms forautomatically detecting and cleansing erroneous concepts in anaggregated knowledge base.

Decision-support systems exist in many different industries where humanexperts require assistance in retrieving and analyzing information. Anexample that will be used throughout this application is a diagnosissystem employed in the healthcare industry. Diagnosis systems can beclassified into systems that use structured knowledge, systems that useunstructured knowledge, and systems that use clinical decision formulas,rules, trees, or algorithms. The earliest diagnosis systems usedstructured knowledge or classical, manually constructed knowledge bases.The Internist-I system developed in the 1970s uses disease-findingrelations and disease-disease relations. The MYCIN system for diagnosinginfectious diseases, also developed in the 1970s, uses structuredknowledge in the form of production rules, stating that if certain factsare true, then one can conclude certain other facts with a givencertainty factor. DXplain, developed starting in the 1980s, usesstructured knowledge similar to that of Internist-I, but adds ahierarchical lexicon of findings.

Iliad, developed starting in the 1990s, adds more sophisticatedprobabilistic reasoning where each disease has an associated a prioriprobability of the disease (in the population for which Iliad wasdesigned), and a list of findings along with the fraction of patientswith the disease who have the finding (sensitivity), and the fraction ofpatients without the disease who have the finding (1-specificity).

In 2000, diagnosis systems using unstructured knowledge started toappear. These systems use some structuring of knowledge such as, forexample, entities such as findings and disorders being tagged indocuments to facilitate retrieval. ISABEL, for example, uses Autonomyinformation retrieval software and a database of medical textbooks toretrieve appropriate diagnoses given input findings. Autonomy Auminenceuses the Autonomy technology to retrieve diagnoses given findings andorganizes the diagnoses by body system. First CONSULT allows one tosearch a large collection of medical books, journals, and guidelines bychief complaints and age group to arrive at possible diagnoses. PortableEmergency Physician Information Database (PEPID) differential diagnosis(DDX) is a diagnosis generator based on PEPID's independent clinicalcontent.

Clinical decision rules have been developed for a number of medicaldisorders, and computer systems have been developed to helppractitioners and patients apply these rules. The Acute Cardiac IschemiaTime-Insensitive Predictive Instrument (ACI-TIPI) takes clinical andelectrocardiogram (ECG) features as input and produces probability ofacute cardiac ischemia as output to assist with triage of patients withchest pain or other symptoms suggestive of acute cardiac ischemia.ACI-TIPI is incorporated into many commercial heartmonitors/defibrillators. The CaseWalker system uses a four-itemquestionnaire to diagnose major depressive disorder. TheProblem-Knowledge Couplers® (PKC) Advisor provides guidance on 98patient problems such as abdominal pain and vomiting.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising at least one processor and at least onememory, the at least one memory comprising instructions executed by theat least one processor to cause the at least one processor to implementa natural language processing system that automatically detects andcleanses erroneous concepts in an aggregated knowledge base. Theillustrative embodiment receives a portion of natural language contentrelated to a selected concept from a knowledge base. The illustrativeembodiment generates a graph data structure representing the conceptpresent in the portion of the natural language content. In theillustrative embodiment, nodes of the graph data structure comprise afirst node representing a name of the concept and a one or more othernodes representing synonyms associated with the first node and, in theillustrative embodiment, the graph data structure further indicatesrelationships between the first node and the one or more other nodes.The illustrative embodiment analyzes the graph data structure todetermine whether or not the graph data structure comprises one or moreconcept conflicts in association with a set of nodes in the graph datastructure. In the illustrative embodiment, the one or more conceptconflicts are associated with the set of nodes if two or more nodesrepresent separate and distinct concepts. The illustrative embodimentsplits the two or more nodes into separate distinct concepts within theknowledge base in response to determining that there are one or moreconcept conflicts due to there being two or more nodes representingseparate and distinct concepts.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive healthcare system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment;

FIG. 4A depicts an exemplary probability transition matrix P for theselected concept in accordance with an illustrative embodiment;

FIG. 4B depicts an exemplary graph data structure with strongconnections for the selected concept in accordance with an illustrativeembodiment;

FIG. 5 illustrates a cognitive healthcare system implementing a Questionand Answer (QA) or request processing pipeline for processing an inputquestion or request in accordance with one illustrative embodiment; and

FIGS. 6A and 6B depict a flowchart of the operation performed by anerroneous concepts detection and cleansing mechanism in accordance withone illustrative embodiment.

DETAILED DESCRIPTION

The strengths of current medical diagnosis, patient health management,and patient treatment recommendation systems are that they may improvemedical practitioners' diagnostic hypotheses, may help medicalpractitioners avoid missing important diagnoses, and may assist medicalpractitioners with determining appropriate treatments for specificdiseases. However, current systems still suffer from significantdrawbacks which should be addressed in order to make such systems moreaccurate and usable for a variety of healthcare applications as well asmore representative of the way in which human healthcare practitionersdiagnose and treat patients. In particular, one drawback of currentsystems is that the vast knowledge base, which is formed by aggregatingdata from a large number of data sources, is aggregated and normalizedusing a variety of techniques such as common chemical structures, commonsynonyms, relationships, or the like. Unfortunately, each data sourceutilizes terms that are specific to that data source but, when combinedwith other data sources, may cause concept conflicts such as noisysynonyms, ambiguous abbreviations, incorrect relationships, or the like.A concept is a collection of a canonical name along with a set ofsynonyms, such as, for example, high blood pressure, hypertension, andarterial hypertension together represents a concept. A canonical name isa preferred real-world name for an entity, for example, high bloodpressure is a canonical name for hypertension. When used in currentmedical diagnosis, patient health management, and patient treatmentrecommendation systems, these concept conflicts may lead to erroneousresults, which has resulted in degrading the quality and usefulness ofcurrent medical diagnosis, patient health management, and patienttreatment recommendation systems.

An example of a concept conflict, specifically an ambiguousabbreviation, is the abbreviation RA. In one data source, theabbreviation RA is synonymous with the medical term rheumatoid arthritisthat is an autoimmune disease in which the body's immune system, whichnormally protects its health by attacking foreign substances likebacteria and viruses, mistakenly attacks the joints within the body. Inanother data source, the abbreviation RA is synonymous with the medicalterm refractory anemia that is any of a group of anemic conditions thatis not associated with another disease and that is marked by apersistent, frequently advanced anemia that can only be successfullytreated through blood transfusions. However, during ingestion of thedata sources into a knowledge base, RA is associated with rheumatoidarthritis and refractory anemia. Thus, when a researcher is utilizingcurrent medical diagnosis, patient health management, and patienttreatment recommendation systems to identify a medication used to treatrefractory anemia, the current medical diagnosis, patient healthmanagement, and patient treatment recommendation systems may identifythe medication Cortisone, which is a synthetically produced medicationused as an anti-inflammatory and anti-allergy agent, i.e. used to treatrheumatoid arthritis and should not be used to treat refractory anemia.Thus, a RA concept conflict exists within the knowledge base.

As another example of a concept conflict, specifically noisy synonyms,is the term medical term of cancer. In one data source, cancer isutilized as a synonym for ovarian cancer, which is a type of cancer thatbegins in the ovaries. In another data source, cancer is utilized as asynonym for kidney cancer, also known as renal cancer, which is a typeof cancer that starts in the cells in the kidney. However, duringingestion of the data sources into a knowledge base, since cancer isused as a synonym for both ovarian cancer and kidney cancer, the kidneycancer may be made incorrectly synonymous with ovarian cancer. Thus,when a researcher is utilizing current medical diagnosis, patient healthmanagement, and patient treatment recommendation systems to identify amedication to treat kidney cancer, the current medical diagnosis,patient health management, and patient treatment recommendation systemsmay identify the medication Cisplatin, which is a chemotherapy drug usedto treat ovarian cancer and is known to have toxic effects on kidneys.Thus, using the term cancer as a synonymous term for any type of cancerwould be incorrect in medical diagnosis, patient health management, andpatient treatment recommendation systems.

Therefore, the illustrative embodiments provide mechanisms forautomatically detecting and cleansing erroneous concepts in anaggregated knowledge base. The mechanisms receive a portion of naturallanguage content from the knowledge base. The mechanisms generate agraph data structure representing a selected concept present in theportion of the natural language content within a knowledge base, wherenodes of the graph data structure represent a canonical name for theselected concept and a set of synonyms identified from a knowledge baseas being associated with the canonical name and edges representrelationships between the canonical name and the set of synonymsidentified from a knowledge base as being associated with the canonicalname. The mechanisms analyze the graph data structure to determinewhether or not the graph data structure comprises one or more conceptconflicts in association with a node in the graph data structure, wherethe one or more concept conflicts are associated with the selectedconcept if at least two nodes in the graph data structure representseparate distinct concepts. Responsive to determining that there are oneor more concept conflicts due to there being separate distinct conceptsassociated with the selected concept, the mechanisms split the selectedconcept into separate distinct concepts within the knowledge base.

Before beginning the detailed discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

As noted above, the present invention provides mechanisms forautomatically detecting and cleansing erroneous concepts in anaggregated knowledge base. The illustrative embodiments may be utilizedin many different types of data processing environments. In order toprovide a context for the description of the specific elements andfunctionality of the illustrative embodiments, FIGS. 1-5 are providedhereafter as example environments in which aspects of the illustrativeembodiments may be implemented. It should be appreciated that FIGS. 1-5are only examples and are not intended to assert or imply any limitationwith regard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIGS. 1-5 are directed to describing an example cognitive system forhealthcare applications (also referred to herein as a “healthcarecognitive system”) which implements a request processing pipeline, suchas a Question Answering (QA) pipeline (also referred to as aQuestion/Answer pipeline or Question and Answer pipeline) for example,request processing methodology, and request processing computer programproduct with which the mechanisms of the illustrative embodiments areimplemented. These requests may be provided as structured orunstructured request messages, natural language questions, or any othersuitable format for requesting an operation to be performed by thehealthcare cognitive system. As described in more detail hereafter, theparticular healthcare application that is implemented in the cognitivesystem of the present invention is a healthcare application forautomatically detecting and cleansing erroneous concepts in anaggregated knowledge base.

It should be appreciated that the healthcare cognitive system, whileshown as having a single request processing pipeline in the exampleshereafter, may in fact have multiple request processing pipelines. Eachrequest processing pipeline may be separately trained and/or configuredto process requests associated with different domains or be configuredto perform the same or different analysis on input requests (orquestions in implementations using a QA pipeline), depending on thedesired implementation. For example, in some cases, a first requestprocessing pipeline may be trained to operate on input requests directedto a first medical malady domain (e.g., various types of blood diseases)while another request processing pipeline may be trained to answer inputrequests in another medical malady domain (e.g., various types ofcancers). In other cases, for example, the request processing pipelinesmay be configured to provide different types of cognitive functions orsupport different types of healthcare applications, such as one requestprocessing pipeline being used for patient diagnosis, another requestprocessing pipeline being configured for medical treatmentrecommendation, another request processing pipeline being configured forpatient monitoring, etc.

Moreover, each request processing pipeline may have their own associatedcorpus within a larger knowledge base or corpora that they ingest andoperate on, e.g., one corpus for blood disease domain documents andanother corpus for cancer diagnostics domain related documents in theabove examples. In some cases, the request processing pipelines may eachoperate on the same domain of input questions but may have differentconfigurations, e.g., different annotators or differently trainedannotators, such that different analysis and potential answers aregenerated. The healthcare cognitive system may provide additional logicfor routing input questions to the appropriate request processingpipeline, such as based on a determined domain of the input request,combining and evaluating final results generated by the processingperformed by multiple request processing pipelines, and other controland interaction logic that facilitates the utilization of multiplerequest processing pipelines.

As noted above, one type of request processing pipeline with which themechanisms of the illustrative embodiments may be utilized is a QuestionAnswering (QA) pipeline. The description of example embodiments of thepresent invention hereafter will utilize a QA pipeline as an example ofa request processing pipeline that may be augmented to includemechanisms in accordance with one or more illustrative embodiments. Itshould be appreciated that while the present invention will be describedin the context of the cognitive system implementing one or more QApipelines that operate on an input question, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may operate on requests that are not posed as“questions” but are formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system. For example, rather than askinga natural language question of “What diagnosis applies to patient P?”,the cognitive system may instead receive a request of “generatediagnosis for patient P,” or the like. It should be appreciated that themechanisms of the QA system pipeline may operate on requests in asimilar manner to that of input natural language questions with minormodifications. In fact, in some cases, a request may be converted to anatural language question for processing by the QA system pipelines ifdesired for the particular implementation.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof these QA pipeline, or request processing pipeline, mechanisms of ahealthcare cognitive system with regard to automatically detecting andcleansing erroneous concepts in an aggregated knowledge base bygenerating a graph data structure representing a selected conceptpresent in portion of natural language content within the knowledgebase, where nodes of the graph data structure represent a canonical namefor the concept and a set of synonyms identified from a knowledge baseas being associated with the canonical name and edges representrelationships between the canonical name and the set of synonyms. Themechanisms then analyze the graph data structure to determine whether ornot the graph data structure comprises one or more concept conflicts inassociation with a node in the graph data structure, where the one ormore concept conflicts are associated with the selected concept if atleast two nodes in the graph data structure represent separate distinctconcepts. Responsive to determining that there are one or more conceptconflicts due to there being separate distinct concepts associated withthe selected concept, the mechanisms split the selected concept intoseparate distinct concepts within the knowledge base.

Thus, it is important to first have an understanding of how cognitivesystems and question and answer creation in a cognitive systemimplementing a QA pipeline is implemented before describing how themechanisms of the illustrative embodiments are integrated in and augmentsuch cognitive systems and request processing pipeline, or QA pipeline,mechanisms. It should be appreciated that the mechanisms described inFIGS. 1-5 are only examples and are not intended to state or imply anylimitation with regard to the type of cognitive system mechanisms withwhich the illustrative embodiments are implemented. Many modificationsto the example cognitive system shown in FIGS. 1-5 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)system based logic, for example, and machine learning logic, which maybe provided as specialized hardware, software executed on hardware, orany combination of specialized hardware and software executed onhardware. The logic of the cognitive system implements the cognitiveoperation(s), examples of which include, but are not limited to,question answering, identification of related concepts within differentportions of content in a corpus, intelligent search algorithms, such asInternet web page searches, for example, medical diagnostic andtreatment recommendations, and other types of recommendation generation,e.g., items of interest to a particular user, potential new contactrecommendations, or the like.

IBM Watson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like high accuracy at speeds far faster than human beings andon a larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding;    -   Ingest and process vast amounts of structured and unstructured        data;    -   Generate and evaluate hypothesis;    -   Weigh and evaluate responses that are based only on relevant        evidence;    -   Provide situation-specific advice, insights, and guidance;    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes;    -   Enable decision making at the point of impact (contextual        guidance);    -   Scale in proportion to the task;    -   Extend and magnify human expertise and cognition;    -   Identify resonating, human-like attributes and traits from        natural language;    -   Deduce various language specific or agnostic attributes from        natural language;    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall);    -   Predict and sense with situational awareness that mimic human        cognition based on experiences; and/or    -   Answer questions based on natural language and specific        evidence.

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system) and/or process requests which may or maynot be posed as natural language questions. The QA pipeline or system isan artificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area, e.g., financial domain, medical domain, legaldomain, etc., where the body of knowledge (knowledge base) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to a cognitive system which implements theQA pipeline. The QA pipeline then answers the input questions using thecontent in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using Natural LanguageProcessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identify thesequestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having alargest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a request processing pipeline 108,which in some embodiments may be a question answering (QA) pipeline, ina computer network 102. For purposes of the present description, it willbe assumed that the request processing pipeline 108 is implemented as aQA pipeline that operates on structured and/or unstructured requests inthe form of input questions. One example of a question processingoperation which may be used in conjunction with the principles describedherein is described in U.S. Patent Application Publication No.2011/0125734, which is herein incorporated by reference in its entirety.The cognitive system 100 is implemented on one or more computing devices104 (comprising one or more processors and one or more memories, andpotentially any other computing device elements generally known in theart including buses, storage devices, communication interfaces, and thelike) connected to the computer network 102. The network 102 includesmultiple computing devices 104 in communication with each other and withother devices or components via one or more wired and/or wireless datacommunication links, where each communication link comprises one or moreof wires, routers, switches, transmitters, receivers, or the like. Thecognitive system 100 and network 102 enables question processing andanswer generation (QA) functionality for one or more cognitive systemusers via their respective computing devices 110-112. Other embodimentsof the cognitive system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receive inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, a corpus of electronicdocuments 106, cognitive system users, and/or other data and otherpossible sources of input. In one embodiment, some or all of the inputsto the cognitive system 100 are routed through the network 102. Thevarious computing devices 104 on the network 102 include access pointsfor content creators and QA system users. Some of the computing devices104 include devices for a database storing the corpus of data 106 (whichis shown as a separate entity in FIG. 1 for illustrative purposes only).Portions of the corpus of data 106 may also be provided on one or moreother network attached storage devices, in one or more databases, orother computing devices not explicitly shown in FIG. 1. The network 102includes local network connections and remote connections in variousembodiments, such that the cognitive system 100 may operate inenvironments of any size, including local and global, e.g., theInternet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. QA system usersaccess the cognitive system 100 via a network connection or an Internetconnection to the network 102, and input questions to the cognitivesystem 100 that are answered by the content in the corpus of data 106.In one embodiment, the questions are formed using natural language. Thecognitive system 100 parses and interprets the question via a QApipeline 108, and provides a response to the cognitive system user,e.g., cognitive system user 110, containing one or more answers to thequestion. In some embodiments, the cognitive system 100 provides aresponse to users in a ranked list of candidate answers while in otherillustrative embodiments, the cognitive system 100 provides a singlefinal answer or a combination of a final answer and ranked listing ofother candidate answers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 106. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data106. The QA pipeline 108 will be described in greater detail hereafterwith regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question which it then parses to extract the majorfeatures of the question, which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the QA pipeline of the IBM Watson™ cognitive system hasregarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers to generate a ranked listing of candidateanswers which may then be presented to the user that submitted the inputquestion, or from which a final answer is selected and presented to theuser. More information about the QA pipeline of the IBM Watson™cognitive system may be obtained, for example, from the IBM Corporationwebsite, IBM Redbooks, and the like. For example, information about theQA pipeline of the IBM Watson™ cognitive system can be found in Yuan etal., “Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

As noted above, while the input to the cognitive system 100 from aclient device may be posed in the form of a natural language question,the illustrative embodiments are not limited to such. Rather, the inputquestion may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a healthcare based cognitive system, this analysis may involveprocessing patient medical records, medical guidance documentation fromone or more corpora, and the like, to provide a healthcare orientedcognitive system result.

In the context of the present invention, cognitive system 100 mayprovide a cognitive functionality for assisting with healthcare basedoperations. For example, depending upon the particular implementation,the healthcare based operations may comprise patient diagnostics,medical treatment recommendation systems, medical practice managementsystems, personal patient care plan generation and monitoring, patientelectronic medical record (EMR) evaluation for various purposes, such asfor identifying patients that are suitable for a medical trial or aparticular type of medical treatment, or the like. Thus, the cognitivesystem 100 may be a healthcare cognitive system 100 that operates in themedical or healthcare type domains and which may process requests forsuch healthcare operations via the request processing pipeline 108 inputas either structured or unstructured requests, natural language inputquestions, or the like. In one illustrative embodiment, the cognitivesystem 100 is an erroneous concept detection and cleansing system for aknowledge base that splits multiple concepts that are identified asbeing associated with a single node into multiple separate concepts.

In an illustrative embodiment, the cognitive system 100 implementserroneous concepts detection and cleansing mechanism 120 forautomatically detecting and cleansing erroneous concepts in anaggregated knowledge base 130. Knowledge base 130 is a body of knowledgeabout the domain, or subject matter area, e.g., financial domain,medical domain, legal domain, etc., where the body of knowledge(knowledge base) can be organized in a variety of configurations, e.g.,a structured repository of domain-specific information, such asontologies, or unstructured data related to the domain, or a collectionof natural language documents about the domain. Knowledge base 130accesses data in one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus of data/information within the corpora ofdata/information.

In one embodiment, erroneous concepts detection and cleansing mechanism120 receives a portion of natural language content from knowledge base130. Erroneous concepts detection and cleansing mechanism 120 generatesa graph data structure representing a concept present in the portion ofthe natural language content, where nodes of the graph data structurerepresent a canonical name for the selected concept and a set ofsynonyms identified from a knowledge base as being associated with thecanonical name and edges represent relationships between the canonicalname and the set of synonyms. Erroneous concepts detection and cleansingmechanism 120 analyzes the graph data structure to determine whether ornot the graph data structure comprises one or more concept conflicts inassociation with a node in the graph data structure, where the one ormore concept conflicts are associated with the selected concept if atleast two nodes in the graph data structure represent separate distinctconcepts. Responsive to determining that there are one or more conceptconflicts due to there being separate distinct concepts associated withthe selected concept, erroneous concepts detection and cleansingmechanism 120 splits the selected concept into separate distinctconcepts within the knowledge base.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 2 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which, which implements acognitive system 100 and QA system pipeline 108 augmented to include theadditional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and Memory Controller Hub (NB/MCH)202 and South Bridge and Input/Output (I/O) Controller Hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 10®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System P® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment. The example diagram of FIG. 3 depicts an implementation of ahealthcare cognitive system 300 that is configured to provide medicaltreatment recommendations for patients. However, it should beappreciated that this is only an example implementation and otherhealthcare operations may be implemented in other embodiments of thehealthcare cognitive system 300 without departing from the spirit andscope of the present invention.

As shown in FIG. 3, in accordance with one illustrative embodiment, uponreceiving a portion of natural language content from a knowledge base304, for a selected concept, which is a collection of a canonical namefor the selected concept and a set of synonyms identified from theknowledge base 304 as being associated with the canonical name,erroneous concepts detection and cleansing mechanism 302 withinhealthcare cognitive system 300 generates a graph data structure andrelated weighted matrix W. The graph data structure comprises a set ofnodes, each node identifying either the canonical name or synonym fromthe set of synonyms identified from knowledge base 304 as beingassociated with the canonical name, and a set of edges representingrelationships between the canonical name and the set of synonyms.Canonical names or synonyms that are abbreviations may be filteredbefore computing W. The abbreviations may then be assigned to theclosest cluster (or clusters in case of ties) once detection andcleansing mechanism 302 has performed cleansing on the rest of the nodesof the concept. For a simple example, high blood pressure is thecanonical name for hypertension and thus, the canonical name of highblood pressure and the related synonyms of hypertension and arterialhypertension together represent a concept. However, due to noise inknowledge base 304, the canonical name of high blood pressure also hasrelated synonyms of thyroiditis, autoimmune thyroiditis, and Hashimoto'sstruma.

The weighted matrix W generated by erroneous concepts detection andcleansing mechanism 302 is a n*n matrix where each element in theweighted matrix W represents the similarity between pairs of thecanonical name and the set of synonyms identified from knowledge base304 as being associated with the canonical name. Thus, using (1) highblood pressure, (2) hypertension, (3) arterial hypertension, (4)thyroiditis, (5) autoimmune thyroiditis, and (6) Hashimoto's struma forthe concept of high blood pressure, erroneous concepts detection andcleansing mechanism 302 would generate a 6*6 matrix representing thesimilarity between pairs of the canonical name and the set of synonyms.

For each i_(th) row and j_(th) column, erroneous concepts detection andcleansing mechanism 302 computes a weight representing an edge weightbetween node i and node j using a function(f) of a feature set:W _(ij) =f(feature set)where a feature set is similarities computed using contextual featuresof member i, contextual features of member j, edit-distance between iand j, domain specific attributes of member i, domain specificattributes of member j, or the like. As is further illustrated, a highweight W_(ij) indicates that node i and node j are strongly related,while a low weight W_(ij) indicates that node i and node j are weaklyrelated. Notably, the set of values from a selected row equals to one.

Thus, using the weighted matrix W, erroneous concepts detection andcleansing mechanism 302 generates a probability transition matrix P,where each element P_(ij) in probability transition matrix P is computedusing:P _(ij) =w _(ij) /Σj w _(ij).FIG. 4A depicts an exemplary probability transition matrix P for theselected concept in accordance with an illustrative embodiment. Notably,with the probability calculated, the set of values from a selected rownow equal one. Furthermore, based on the calculated probabilities, asshown in FIG. 4B in accordance with an illustrative embodiment, strongconnections may be implied between (1) high blood pressure, (2)hypertension, and (3) arterial hypertension as well as separatelybetween (4) thyroiditis, (5) autoimmune thyroiditis, and (6) Hashimoto'sstruma in the graph data structure related to the weighted matrix W.

In order to determine/verify whether the given concept consists ofmultiple concepts, erroneous concepts detection and cleansing mechanism302 then performs teleporting random walks on probability transitionmatrix P. A teleporting random walk on P is irreducible, aperiodic andergodic. Hence, probability transition matrix P has a unique stationarydistribution. Erroneous concepts detection and cleansing mechanism 302selects an item or canonical name (S₁) with the largest stationarydistribution value from the stationary distribution of visitingprobabilities. When two or more nodes receive the same number ofstationary probabilities, detection and cleansing mechanism 302 randomlychooses one of the two or more nodes.

In the next operation, erroneous concepts detection and cleansingmechanism 302 uses node 1 as an absorbing state node t where, in amodified probability transition matrix P, the probability at P_(tt) (theintersection of node 1 with node 1, the absorbing state t, in modifiedprobability transition matrix P) is equal to 1 and the probability atP_(tg) (the intersection of node 1, the absorbing state t, with anyother node g in modified probability transition matrix P) is equal to 0,where g is not equal to t.

Using the modified probability transition matrix P, erroneous conceptsdetection and cleansing mechanism 302 calculates the expected number ofvisits for all the nodes. Thus, the node with the highest number ofvisits is chosen as the next item/canonical name, for example, node 4may have the next highest expected number of visits. That is, nodesstrongly connected to the absorbing state node (node 1) will have fewernumber of visits because the walk tends to be absorbed soon aftervisiting them. In contradistinction, groups of nodes far away from theabsorbing state node (node 1) allow the random walks to linger amongthem, thus have more visits, for example, node 4.

With the two selected items, node 1 and node 4, erroneous conceptsdetection and cleansing mechanism 302 assigns the rest of the nodes,nodes 2, 3, 5, and 6, to the closest node, i.e. node 1 or node 4 basedon some similarity measure, such as cosine similarity, Euclideandistance, or the like. Thus, in one example, erroneous conceptsdetection and cleansing mechanism 302 assigns nodes 2 and 3 to node 1based on closeness and assigns nodes 5 and 6 to node 4 based oncloseness.

With nodes 1 and 4 identified as canonical name nodes, i.e. nodes thatrepresent two separate concepts, erroneous concepts detection andcleansing mechanism 302 determines whether there are any other nodesthat should be considered as separate concept(s). In order to determineif the two concepts (with node 1 and node 4 as canonical names orcentral items in the cluster) should be considered as separate conceptsor not, erroneous concepts detection and cleansing mechanism 302computes an inter-cluster distance between pairs of clusters obtained sofar (here, two clusters with node 1 and node 4 as central items). If aminimum inter-cluster distance between the newly added concept (node 4and nodes assigned to it) with respect to the concepts alreadyidentified (node 1 and nodes assigned to it) is less than or equal to auser specified threshold, then erroneous concepts detection andcleansing mechanism 302 stops the process of detecting erroneousconcepts and identifies clusters obtained in the previous iteration asthe concepts. For example, if the minimum inter-cluster distance betweenthe two concepts is less than the user specified threshold, theerroneous concepts detection and cleansing mechanism 302 stops andreturns only one concept (node 1 and all other nodes assigned to it). Ifthe minimum inter-cluster distance between the two concepts is greaterthan the user specified threshold, the erroneous concepts detection andcleansing mechanism 302 considers the two concepts as separate conceptsand continues to find the next concept. Based on this identification,erroneous concepts detection and cleansing mechanism 302 performs one ormore operation within knowledge base 304 to split the canonical namesand their related synonyms such that the two concepts are no longerrelated within knowledge base 304. That is, erroneous concepts detectionand cleansing mechanism 302 performs one or more operations such that,in keeping with the example above, node 1 associated with the canonicalname high blood pressure has synonyms of hypertension and arterialhypertension (nodes 2 and 3, respectively) and, in a completely separateconcept, node 4 associated with the canonical name thyroiditis hassynonyms of autoimmune thyroiditis and Hashimoto's struma (nodes 5 and6, respectively).

However, if the minimum inter-cluster distance between the clustersformed so far is less than the user specified threshold, erroneousconcepts detection and cleansing mechanism 302 stops and returns theclusters that were obtained from the previous iteration. Alternativestopping criteria could be based on average inter-cluster distancesbetween pairs of nodes. In one example, erroneous concepts detection andcleansing mechanism 302 sums the inter-cluster distances between eachnode in Cluster1 (nodes 1, 2, and 3) and each node in Cluster2 (nodes 4,5, and 6):sum of inter-clusterdistances=(dist(Node1,Node6)+dist(Node1,Node5)+dist(Node1,Node4)+dist(Node2,Node6)+dist(Node2,Node5)+dist(Node2,Node4)+dist(Node3,Node6)+dist(Node3,Node5)+dist(Node3,Node4)).

Then, erroneous concepts detection and cleansing mechanism 302 finds theaverage inter-cluster distance:average inter-cluster distance=sum of inter-cluster distances/number ofsummed distances.

Erroneous concepts detection and cleansing mechanism 302 then uses node4 as a new absorbing state t where, in a newly modified probabilitytransition matrix P, the probability at P_(tt) (the intersection of node4 with node 4, the absorbing state t, in modified probability transitionmatrix P) is equal to 1 and the probability at P_(tg) (the intersectionof node 4, the absorbing state t, with any other node g in modifiedprobability transition matrix P) is equal to 0, where g is not equal tot.

Using the modified probability transition matrix P, erroneous conceptsdetection and cleansing mechanism 302 calculates the expected number ofvisits between each remaining node. Thus, the next node with the highestnumber of visits is chosen as the next canonical name, for example, node6 has the next highest number of visits. That is, nodes stronglyconnected to the absorbing state node (node 6) will have fewer number ofvisits because the walk tends to be absorbed soon after visiting them.In contradistinction, groups of nodes far away from the absorbing statenodes (nodes 1 and 4) allow the random walks to linger among them, thushave more visits, for example, node 6.

With three highest visited nodes identified, i.e. node 1, node 4, andnode 6, erroneous concepts detection and cleansing mechanism 302 assignsthe rest of the nodes, nodes 2, 3, and 5 to the closest node, i.e. node1, node 4, or node 6 based on some similarity measure, such as cosinesimilarity, Euclidean distance, or the like. Thus, in one example,erroneous concepts detection and cleansing mechanism 302 assigns nodes 2and 3 to node 1 based on closeness, assigns node 5 to node 4 based oncloseness, and does not assign any other node to node 6. With nodes 1,4, and 6 identified as canonical name nodes, i.e. nodes that representthree separate concepts, erroneous concepts detection and cleansingmechanism 302 repeats the process of computing an inter-cluster distancebetween pairs of clusters obtained so far and comparing a minimuminter-cluster distance to a user specified threshold, as describedpreviously.

With the detection and cleansing of erroneous concepts within knowledgebase 304 performed by erroneous concepts detection and cleansingmechanism 302, healthcare cognitive system 300 may better answerquestions that are received by a QA pipeline of healthcare cognitivesystem 300. FIG. 5 illustrates a QA pipeline of a healthcare cognitivesystem, such as healthcare cognitive system 300 in FIG. 3, or animplementation of cognitive system 100 in FIG. 1, for processing aninput question in accordance with one illustrative embodiment. It shouldbe appreciated that the stages of the QA pipeline shown in FIG. 5 areimplemented as one or more software engines, components, or the like,which are configured with logic for implementing the functionalityattributed to the particular stage. Each stage is implemented using oneor more of such software engines, components or the like. The softwareengines, components, etc. are executed on one or more processors of oneor more data processing systems or devices and utilize or operate ondata stored in one or more data storage devices, memories, or the like,on one or more of the data processing systems. The QA pipeline of FIG. 5is augmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 500 may be provided for interfacingwith the pipeline 500 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 5, the QA pipeline 500 comprises a plurality of stages510-580 through which the cognitive system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 510, the QA pipeline 500 receives an input question that ispresented in a natural language format. That is, a user inputs, via auser interface, an input question for which the user wishes to obtain ananswer, e.g., “What medical treatments for diabetes are applicable to a60 year old patient with cardiac disease?” In response to receiving theinput question, the next stage of the QA pipeline 500, i.e. the questionand topic analysis stage 520, parses the input question using naturallanguage processing (NLP) techniques to extract major features from theinput question, and classify the major features according to types,e.g., names, dates, or any of a plethora of other defined topics. Forexample, in a question of the type “Who were Washington's closestadvisors?”, the term “who” may be associated with a topic for “persons”indicating that the identity of a person is being sought, “Washington”may be identified as a proper name of a person with which the questionis associated, “closest” may be identified as a word indicative ofproximity or relationship, and “advisors” may be indicative of a noun orother language topic. Similarly, in the previous question “medicaltreatments” may be associated with pharmaceuticals, medical procedures,holistic treatments, or the like, “diabetes” identifies a particularmedical condition, “60 years old” indicates an age of the patient, and“cardiac disease” indicates an existing medical condition of thepatient.

In addition, the extracted major features include key words and phrases,classified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referred to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?,” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “drug”since if this word were replaced with the answer, e.g., the answer“Adderall” can be used to replace the term “drug” to generate thesentence “Adderall has been shown to relieve the symptoms of ADD withrelatively few side effects.” The focus often, but not always, containsthe LAT. On the other hand, in many cases it is not possible to infer ameaningful LAT from the focus.

Referring again to FIG. 5, the identified major features are then usedduring the question decomposition stage 530 to decompose the questioninto one or more queries that are applied to knowledge base or corpora545 in order to generate one or more hypotheses. The queries aregenerated in any known or later developed query language, such as theStructure Query Language (SQL), or the like. The queries are applied toone or more databases storing information about the electronic texts,documents, articles, websites, and the like, that make up a corpora ofdata/information within knowledge base/corpora 545. That is, thesevarious sources themselves, different collections of sources, and thelike, represent a different corpus 547 within the knowledge base/corpora545. There may be different corpora 547 defined for differentcollections of documents based on various criteria depending upon theparticular implementation. For example, different corpora may beestablished for different topics, subject matter categories, sources ofinformation, or the like. As one example, a first corpus may beassociated with healthcare documents while a second corpus may beassociated with financial documents. Alternatively, one corpus may bedocuments published by the U.S. Department of Energy while anothercorpus may be IBM Redbooks documents. Any collection of content havingsome similar attribute may be considered to be a corpus 547 within theknowledge base/corpora 545.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 540 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 540, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 540, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA pipeline 500, in stage 550, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this involvesusing a plurality of reasoning algorithms, each performing a separatetype of analysis of the language of the input question and/or content ofthe corpus that provides evidence in support of, or not in support of,the hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In generally, however,these algorithms look for particular terms, phrases, or patterns of textthat are indicative of terms, phrases, or patterns of interest anddetermine a degree of matching with higher degrees of matching beinggiven relatively higher scores than lower degrees of matching.

Thus, for example, an algorithm may be configured to look for the exactterm from an input question or synonyms to that term in the inputquestion, e.g., the exact term or synonyms for the term “movie,” andgenerate a score based on a frequency of use of these exact terms orsynonyms. In such a case, exact matches will be given the largestscores, while synonyms may be given lower scores based on a relativeranking of the synonyms as may be specified by a subject matter expert(person with knowledge of the particular domain and terminology used) orautomatically determined from frequency of use of the synonym in thecorpus corresponding to the domain. Thus, for example, an exact match ofthe term “movie” in content of the corpus (also referred to as evidence,or evidence passages) is given a largest score. A synonym of movie, suchas “motion picture” may be given a lower score but still higher than asynonym of the type “film” or “moving picture show.” Instances of theexact matches and synonyms for each evidence passage may be compiled andused in a quantitative function to generate a score for the degree ofmatching of the evidence passage to the input question.

Thus, for example, a hypothesis or candidate answer to the inputquestion of “What was the first movie?” is “The Horse in Motion.” If theevidence passage contains the statements “The first motion picture evermade was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was amovie of a horse running,” and the algorithm is looking for exactmatches or synonyms to the focus of the input question, i.e. “movie,”then an exact match of “movie” is found in the second sentence of theevidence passage and a highly scored synonym to “movie,” i.e. “motionpicture,” is found in the first sentence of the evidence passage. Thismay be combined with further analysis of the evidence passage toidentify that the text of the candidate answer is present in theevidence passage as well, i.e. “The Horse in Motion.” These factors maybe combined to give this evidence passage a relatively high score assupporting evidence for the candidate answer “The Horse in Motion” beinga correct answer.

It should be appreciated that this is just one simple example of howscoring can be performed. Many other algorithms of various complexitymay be used to generate scores for candidate answers and evidencewithout departing from the spirit and scope of the present invention.

In the synthesis stage 560, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 500 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonym may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 500 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 500 has about the evidence that the candidate answer isinferred by the input question, i.e. that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 570 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 580, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIGS. 6A and 6B depict a flowchart of the operation performed by anerroneous concepts detection and cleansing mechanism, such as erroneousconcepts detection and cleansing mechanism 302 of FIG. 3, in accordancewith one illustrative embodiment. As the operation begins, the erroneousconcepts detection and cleansing mechanism receives a portion of naturallanguage content from a knowledge base associated with a selectedconcept (step 602). The selected concept comprises a collection of acanonical name and a set of synonyms identified from the knowledge baseas being associated with the canonical name. The erroneous conceptsdetection and cleansing mechanism generates a graph data structure (step604). The graph data structure comprises a set of nodes, each nodeidentifying either the canonical name or synonym from the set ofsynonyms identified from the knowledge base as being associated with thecanonical name, and a set of edges representing relationships betweenthe canonical name and the set of synonyms.

The erroneous concepts detection and cleansing mechanism then generatesa weighted matrix W (step 606). The weighted matrix W is a n*n matrixwhere each element in the weighted matrix W represents the similaritybetween pairs of the canonical name and the set of synonyms identifiedfrom the knowledge base as being associated with the canonical name. Foreach i_(th) row and j_(th) column in the weighted matrix W, theerroneous concepts detection and cleansing mechanism computes a weightrepresenting an edge weight (step 608) between node i and node j using afunction(f) of a feature set:W _(ij) =f(feature set)where a feature set is similarities computed using contextual featuresof member i, contextual features of member j, edit-distance between iand j, domain specific attributes of member i, domain specificattributes of member j, or the like. A high weight W_(ij) indicates thatnode i and node j are strongly related, while a low weight W_(ij)indicates that node i and node j are weakly related.

Using the weighted matrix W, the erroneous concepts detection andcleansing mechanism generates a probability transition matrix P (step610) where each element P_(ij) in probability transition matrix P iscomputed using:P _(ij) =w _(ij)/Σ_(j) w _(ij).In order to determine/verify whether the given concept consists ofmultiple concepts, the erroneous concepts detection and cleansingmechanism performs teleporting random walks on probability transitionmatrix P (step 612). The probability transition matrix P models a randommodel, i.e. nodes that a user will most likely move or teleport to mostoften from another node. Therefore, performing teleporting random walksprobability transition matrix P yields a stationary distribution ofvisiting probabilities of each node and thus, the node that is visitedthe most receives the largest probability. The erroneous conceptsdetection and cleansing mechanism selects a node with the largeststationary probability as the first selected item (step 614). If two ormore nodes have a same stationary distribution value, the erroneousconcepts detection and cleansing mechanism selects one of the two ormore nodes.

The erroneous concepts detection and cleansing mechanism turns therecently selected item into an absorbed state node t in modifiedprobability transition matrix P (step 616). In doing so, the erroneousconcepts detection and cleansing mechanism changes the probability atP_(tt) (the intersection of the selected node and the selected node, theabsorbing state t, in modified probability transition matrix P to 1 andthe probability at P_(tg) (the intersection of selected node, theabsorbing state t, and any other node g in modified probabilitytransition matrix P to 0, where g is not equal to t.

Using the modified probability transition matrix P, the erroneousconcepts detection and cleansing mechanism determines the expectednumber of visits for all the nodes and selects the node with the highestnumber of visits as the next item (step 618). That is, nodes stronglyconnected to the absorbing state node t will have fewer number of visitsbecause the walk tends to be absorbed soon after visiting them. Incontradistinction, groups of nodes far away from the absorbing statenode t allow the random walks to linger among them, thus have morevisits.

With the two highest visited nodes identified, the erroneous conceptsdetection and cleansing mechanism assigns all the unselected nodes toone of the closest nodes in the selected items set to form clusters(step 620) based on some similarity measure, such as cosine similarity,Euclidean distance, or the like. With a set of nodes identified ascanonical name nodes, i.e. nodes that represent two separate concepts,the erroneous concepts detection and cleansing mechanism computes aninter-cluster distance between every pair of clusters obtained from theprevious step (step 622). The erroneous concepts detection and cleansingmechanism then determines whether the average inter-cluster distance isless than or equal to a user specified threshold (step 624). If at step624 the erroneous concepts detection and cleansing mechanism determinesthat the computed inter-cluster distance is less than or equal to theuser specified threshold, the erroneous concepts detection and cleansingmechanism rejects the recently selected item and assign the rejecteditem and its nodes to one of the remaining selected items therebyreturning the clusters obtained in the previous iteration as splitconcepts (step 626), with the operation ending thereafter. If at step624 the erroneous concepts detection and cleansing mechanism determinesthat the computed inter-cluster distance is greater than the userspecified threshold, the operation returns to step 616.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments provide mechanisms for automaticallydetecting and cleansing erroneous concepts in an aggregated knowledgebase. The mechanisms receive a portion of natural language content fromthe knowledge base. The mechanisms generate a graph data structurerepresenting a selected concept present in portion of the naturallanguage content within a knowledge base, where nodes of the graph datastructure represent a canonical name for the selected concept and a setof synonyms identified from a knowledge base as being associated withthe canonical name and edges represent relationships between thecanonical name and the set of synonyms identified from a knowledge baseas being associated with the canonical name. The mechanisms analyze thegraph data structure to determine whether or not the graph datastructure comprises one or more concept conflicts in association with anode in the graph data structure, where the one or more conceptconflicts are associated with the selected concept if at least two nodesin the graph data structure represent separate distinct concepts.Responsive to determining that there are one or more concept conflictsdue to there being separate distinct concepts associated with theselected concept, the mechanisms split the selected concept intoseparate distinct concepts within the knowledge base.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisingat least one processor and at least one memory, the at least one memorycomprising instructions executed by the at least one processor to causethe at least one processor to implement natural language processing(NLP) system, wherein the method comprises: receiving, by the NLPsystem, a portion of natural language content related to a selectedconcept from a knowledge base; generating, by the NLP system, a graphdata structure representing the concept present in the portion of thenatural language content, wherein nodes of the graph data structurecomprise a first node representing a name of the concept and a one ormore other nodes representing synonyms associated with the first nodeand wherein the graph data structure further indicates relationshipsbetween the first node and the one or more other nodes based on asimilarity measure of the other nodes to the first node; analyzing, bythe NLP system, the graph data structure to determine whether or not thegraph data structure comprises one or more concept conflicts inassociation with a set of nodes in the graph data structure, wherein theone or more concept conflicts are associated with the set of nodesresponsive to two or more nodes representing separate and distinctconcepts; and responsive to determining that there are one or moreconcept conflicts due to there being two or more nodes representingseparate and distinct concepts, splitting, by the NLP system, the two ormore nodes into separate distinct concepts within the knowledge base. 2.The method of claim 1, wherein analyzing the graph data structurecomprises: automatically generating, by the NLP system, a first clusterof nodes including the first node and one or more other nodes associatedwith the first node; and storing, by the NLP system, in the knowledgebase, the first cluster of nodes in association with a first separatedistinct concept.
 3. The method of claim 2, wherein automaticallygenerating the cluster comprises: assigning, by the NLP system, othernodes, in the one or more other nodes, to the first cluster of nodesassociated with the first node based on the similarity measure of theother nodes to the first node.
 4. The method of claim 3, wherein thesimilarity measure is a cosine similarity or a Euclidean distance. 5.The method of claim 1, wherein analyzing the graph data structurecomprises: identifying, by the NLP system, a second node in the graphdata structure, wherein the second node is a node that has one or moreother nodes connected to the second node by an edge in the graph and hasa relatively high visiting probability; automatically generating, by theNLP system, a second cluster of nodes including the second node and oneor more other nodes associated with the second node; and storing, by theNLP system, in the knowledge base, the second cluster of nodes inassociation with a second separate distinct concept.
 6. The method ofclaim 5, wherein identifying the second canonical concept node in thegraph comprises: calculating, by the NLP system, for each node in thegraph, a visiting probability indicating a probability that the conceptassociated with the node will be used by a cognitive system to perform acognitive operation; and selecting, by the NLP system, a node having alargest visiting probability as the first node.
 7. The method of claim5, wherein automatically generating the cluster comprises: assigning, bythe NLP system, other nodes, in the one or more other nodes, to thesecond cluster of nodes associated with the second node based on adistance of the other nodes to the second node.
 8. The method of claim7, wherein the similarity measure is a cosine similarity or a Euclideandistance.
 9. A computer program product comprising a computer readablestorage medium having a computer readable program stored therein,wherein the computer readable program, when executed on a computingdevice, causes the computing device to implement a natural languageprocessing (NLP) system which operates to: receive a portion of naturallanguage content related to a selected concept from a knowledge base;generate a graph data structure representing the concept present in theportion of the natural language content, wherein nodes of the graph datastructure comprise a first node representing a name of the concept and aone or more other nodes representing synonyms associated with the firstnode and wherein the graph data structure further indicatesrelationships between the first node and the one or more other nodesbased on a similarity measure of the other nodes to the first node;analyze the graph data structure to determine whether or not the graphdata structure comprises one or more concept conflicts in associationwith a set of nodes in the graph data structure, wherein the one or moreconcept conflicts are associated with the set of nodes responsive to twoor more nodes representing separate and distinct concepts; andresponsive to determining that there are one or more concept conflictsdue to there being two or more nodes representing separate and distinctconcepts, split the two or more nodes into separate distinct conceptswithin the knowledge base.
 10. The computer program product of claim 9,wherein the computer readable program to analyze the graph datastructure further causes the computing device to implement the NLPsystem which operates to: automatically generate a first cluster ofnodes including the first node and one or more other nodes associatedwith the first node; and store, in the knowledge base, the first clusterof nodes in association with a first separate distinct concept.
 11. Thecomputer program product of claim 10, wherein the computer readableprogram to automatically generate the cluster further causes thecomputing device to implement the NLP system which operates to: assignother nodes, in the one or more other nodes, to the first cluster ofnodes associated with the first node based on the similarity measure ofthe other nodes to the first node.
 12. The computer program product ofclaim 9, wherein the computer readable program to analyze the graph datastructure further causes the computing device to implement the NLPsystem which operates to: identify a second node in the graph datastructure, wherein the second node is a node that has one or more othernodes connected to the second node by an edge in the graph and has arelatively high visiting probability; automatically generate a secondcluster of nodes including the second node and one or more other nodesassociated with the second node; and store in the knowledge base, thesecond cluster of nodes in association with a second separate distinctconcept.
 13. The computer program product of claim 12, wherein thecomputer readable program to identify the second canonical concept nodein the graph further causes the computing device to implement the NLPsystem which operates to: calculate for each node in the graph, avisiting probability indicating a probability that the conceptassociated with the node will be used by a cognitive system to perform acognitive operation; and select a node having a largest visitingprobability as the first node.
 14. The computer program product of claim12, wherein the computer readable program to automatically generate thecluster further causes the computing device to implement the NLP systemwhich operates to: assign other nodes, in the one or more other nodes,to the second cluster of nodes associated with the second node based ona distance of the other nodes to the second node.
 15. An apparatuscomprising: a processor; and a memory coupled to the processor, whereinthe memory comprises instructions which, when executed by the processor,cause the processor to implement a natural language processing (NLP)system which operates to: receive a portion of natural language contentrelated to a selected concept from a knowledge base; generate a graphdata structure representing the concept present in the portion of thenatural language content, wherein nodes of the graph data structurecomprise a first node representing a name of the concept and a one ormore other nodes representing synonyms associated with the first nodeand wherein the graph data structure further indicates relationshipsbetween the first node and the one or more other nodes based on asimilarity measure of the other nodes to the first node; analyze thegraph data structure to determine whether or not the graph datastructure comprises one or more concept conflicts in association with aset of nodes in the graph data structure, wherein the one or moreconcept conflicts are associated with the set of nodes responsive to twoor more nodes representing separate and distinct concepts; andresponsive to determining that there are one or more concept conflictsdue to there being two or more nodes representing separate and distinctconcepts, split the two or more nodes into separate distinct conceptswithin the knowledge base.
 16. The apparatus of claim 15, wherein theinstructions to analyze the graph data structure further cause theprocessor to implement the NLP system which operates to: automaticallygenerate a first cluster of nodes including the first node and one ormore other nodes associated with the first node; and store, in theknowledge base, the first cluster of nodes in association with a firstseparate distinct concept.
 17. The apparatus of claim 16, wherein theinstructions to automatically generate the cluster further cause theprocessor to implement the NLP system which operates to: assign othernodes, in the one or more other nodes, to the first cluster of nodesassociated with the first node based on the similarity measure of theother nodes to the first node.
 18. The apparatus of claim 15, whereinthe instructions to analyze the graph data structure further cause theprocessor to implement the NLP system which operates to: identify asecond node in the graph data structure, wherein the second node is anode that has one or more other nodes connected to the second node by anedge in the graph and has a relatively high visiting probability;automatically generate a second cluster of nodes including the secondnode and one or more other nodes associated with the second node; andstore in the knowledge base, the second cluster of nodes in associationwith a second separate distinct concept.
 19. The apparatus of claim 18,wherein the instructions to identify the second canonical concept nodein the graph further cause the processor to implement the NLP systemwhich operates to: calculate for each node in the graph, a visitingprobability indicating a probability that the concept associated withthe node will be used by a cognitive system to perform a cognitiveoperation; and select a node having a largest visiting probability asthe first node.
 20. The apparatus of claim 18, wherein the instructionsto automatically generate the cluster further cause the processor toimplement the NLP system which operates to: assign other nodes, in theone or more other nodes, to the second cluster of nodes associated withthe second node based on a distance of the other nodes to the secondnode.